Video-based activity and behavior analysis for mice has garnered wide attention in biomedical research. Animal facilities hold large numbers of mice housed in `home-cages' densely stored within ventilated racks. Automated analysis of mice activity in their home-cages can provide a new set of sensitive measures for detecting abnormalities and time-resolved deviation from baseline behavior. Large scale monitoring in animal facilities requires minimal footprint hardware that integrates seamlessly with the ventilated racks. Compactness of hardware imposes use of fisheye lenses positioned in close proximity to the cage. In this paper, we estimate the 3D pose of a mouse from fisheye distorted monocular monochromatic images using a novel adaptation of a structured forests algorithm. The method utilizes classification decision trees leveraging their versatility to store arbitrary information in the leaf-nodes. During training, the samples arriving at each node are mapped from continuous pose space to discrete class labels such that similar poses are grouped in the same class. The node splitting function is trained by optimizing a classification objective function rather than a high-dimensional regression one. The leaf-nodes store the pose parameters for the set of samples reaching the node. A prediction model preserving the structural relationship of the pose is formed based on the samples in the leaf-nodes. We apply the method to what we believe is the first known training set for 3D recovery of mouse key points from monocular images. We compare the results of our approach to those obtained via standard regression techniques.